Federated learning (FL) is a promising paradigm for multiple devices to cooperatively train a model. When applied in wireless networks, two issues consistently affect the performance of FL, i.e., data heterogeneity of devices and limited bandwidth. Many papers have investigated device scheduling strategies considering the two issues. However, most of them recognize data heterogeneity as a property of individual devices. In this paper, we prove that the convergence speed of FL is affected by the sum of device-level and sample-level collective gradient divergence (CGD). Device-level CGD refers to the gradient divergence of the scheduled device group, instead of the sum of the individual device divergence. Sample-level CGD is statistically upper bounded by sampling variance, which is inversely proportional to the total number of samples scheduled for local update. To derive a tractable form of the device-level CGD, we further consider classification tasks and transform it into the weighted earth moving distance (WEMD) between the group distribution and the global distribution. Then we propose FedCGD algorithm to minimize the sum of sampling variance and WEMD on classification tasks by device scheduling and bandwidth allocation, within polynomial time. Simulation shows that the proposed strategy increases classification accuracy on the CIFAR-10 dataset by up to 4.2% while scheduling 41.8% fewer devices, and flexibly switches between reducing WEMD and reducing sampling variance.

In edge intelligence systems, task offloading and resource allocation policies critically depend on the required computing capacity of the task, which can only be accurately measured after execution, presenting significant design challenges. In this paper, we address the problem of robust task offloading and resource allocation under imperfect computing capacity information, where the exact value as well as distribution knowledge of the required computing capacity cannot be obtained in advance. Specifically, we formulate theenergy-time cost(ETC) minimization problem using min-max robust optimization. To tackle this challenging issue, we propose a decoupling method. This method first assumes the offloading policy is predetermined and derives two independent subproblems: local ETC and edge ETC. Then, we provide a closed-form optimal solution for the local ETC problem. The edge ETC problem is equivalently transformed into a geometric programming (GP) problem, and we introduce an effective iterative algorithm to obtain a stationary point, utilizing successive convex approximation (SCA). Finally, we design a coordinate descent (CD)-based algorithm to optimize the offloading policy effectively. Extensive simulations demonstrate that the proposed policy significantly outperforms other benchmark methods, achieving near-optimal performance even in the presence of high estimation errors in computing capacity.

Leveraging the computing and sensing capabilities of vehicles, vehicular federated learning (VFL) has been applied to edge training for connected vehicles. The dynamic and inter-connected nature of vehicular networks presents unique opportunities to harness direct vehicle-to-vehicle (V2V) communications, enhancing VFL training efficiency. In this paper, we formulate a stochastic optimization problem to optimize the VFL training performance, considering the energy constraints and mobility of vehicles, and propose a V2V-enhanced dynamic scheduling (VEDS) algorithm to solve it. The model aggregation requirements of VFL and the limited transmission time due to mobility result in a stepwise objective function, which presents challenges in solving the problem. We thus propose a derivative-based drift-plus-penalty method to convert the long-term stochastic optimization problem to an online mixed integer nonlinear programming (MINLP) problem, and provide a theoretical analysis to bound the performance gap between the online solution and the offline optimal solution. Further analysis of the scheduling priority reduces the original problem into a set of convex optimization problems, which are efficiently solved using the interior-point method. Experimental results demonstrate that compared with the state-of-the-art benchmarks, the proposed algorithm enhances the image classification accuracy on the CIFAR-10 dataset by 4.20% and reduces the average displacement errors on the Argoverse trajectory prediction dataset by 9.82%.

In wireless body area networks (WBANs), the deep channel fading between the nodes and the hub significantly impairs the reliability of end-to-end signal transmission. However, some nodes in WBANs necessitate high-priority data transmission with stringent latency and accuracy requirements. Retransmission is ineffective against channel fading and can result in increased communication overhead and extended transmission delays. This paper proposes an opportunistic relay strategy tailored to the characteristics of WBAN nodes with varying priorities. This strategy converts suitable low-priority nodes as relays during high-priority time slots to forward high-priority data to the hub when deep fading occurs. The relay can be opportunistically selected based on the channel condition and the power usage. With Lyanupov optimization, we maximize the transmission reliability while ensuring the extra power consumption of low-priority nodes are acceptable. Subsequently, simulations are conducted in the Network Simulator 3 (NS3) to validate the proposed relay selection strategy, showing that the proposed strategy effectively improves the reliability from 90.2 % to 99.1 % with an additional power consumption of 30%.

With the rapid advancement of intelligent vehicle technology, the demand for vehicular computing power is increasing. To alleviate computing loads on the on-board computer, this paper proposes an in-vehicle distributed computing system that leverages in-vehicle devices, such as smartphones and tablet computers, for cooperative task execution. Different from previous works that focused on the impact of CPU frequency management for computation offloading, we consider the scenario where the CPU frequency of devices cannot be adjusted, and study a more practical approach to make offloading and scheduling decisions by considering the CPU utilization of devices. Therefore, we first derive an analytical relationship between CPU utilization and computation latency, and then propose a CPU Utilization-Aware Scheduling (CUAS) policy to minimize response latency consisting of computation and communication latency. Simulations conducted on Simgrid show that our proposed CUAS policy can reduce the response latency by up to 20.60% compared with the benchmarks. Additionally, we established a real-world testbed to validate our system's practicality. Experimental results indicate that our proposed policy can reduce response latency by up to 20.75% compared with the benchmarks.